BACKGROUND MicroRNAs (miRNAs) play important roles in a variety of biological processes by regulating gene expression at the post-transcriptional level. So, the discovery of new miRNAs has become a popular task in biological research. Since the experimental identification of miRNAs is time-consuming, many computational tools have been developed to identify miRNA precursor (pre-miRNA). Most of these computation methods are based on traditional machine learning methods and their performance depends heavily on the selected features which are usually determined by domain experts. To develop easily implemented methods with better performance, we investigated different deep learning architectures for the pre-miRNAs identification. RESULTS In this work, we applied convolution neural networks (CNN) and recurrent neural networks (RNN) to predict human pre-miRNAs. We combined the sequences with the predicted secondary structures of pre-miRNAs as input features of our models, avoiding the feature extraction and selection process by hand. The models were easily trained on the training dataset with low generalization error, and therefore had satisfactory performance on the test dataset. The prediction results on the same benchmark dataset showed that our models outperformed or were highly comparable to other state-of-the-art methods in this area. Furthermore, our CNN model trained on human dataset had high prediction accuracy on data from other species. CONCLUSIONS Deep neural networks (DNN) could be utilized for the human pre-miRNAs detection with high performance. Complex features of RNA sequences could be automatically extracted by CNN and RNN, which were used for the pre-miRNAs prediction. Through proper regularization, our deep learning models, although trained on comparatively small dataset, had strong generalization ability.

中文翻译：

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用于人类microRNA前体检测的深度神经网络。

背景技术微小RNA（miRNA）通过在转录后水平上调节基因表达在多种生物学过程中起重要作用。因此，发现新的miRNA已成为生物学研究中的一项普遍任务。由于miRNA的实验鉴定非常耗时，因此已经开发出许多计算工具来鉴定miRNA前体（pre-miRNA）。这些计算方法大部分基于传统的机器学习方法，其性能在很大程度上取决于通常由领域专家确定的所选功能。为了开发具有更好性能的易于实施的方法，我们研究了用于pre-miRNA识别的不同深度学习架构。结果在这项工作中，我们应用了卷积神经网络（CNN）和递归神经网络（RNN）来预测人类前miRNA。我们将序列与pre-miRNA的预测二级结构结合起来作为模型的输入特征，从而避免了手工进行特征提取和选择过程。该模型易于在训练数据集上进行训练，且泛化误差低，因此在测试数据集上具有令人满意的性能。在相同基准数据集上的预测结果表明，我们的模型优于或与该领域的其他最新方法具有高度可比性。此外，我们在人类数据集上训练的CNN模型对其他物种的数据具有较高的预测准确性。结论深度神经网络（DNN）可用于人类前miRNA的高性能检测。CNN和RNN可以自动提取RNA序列的复杂特征，用于pre-miRNA的预测。通过适当的规范化，我们的深度学习模型尽管在相对较小的数据集上进行了训练，但具有很强的泛化能力。